Univariate Time Series Analysis

Kevin Sheppard

Forecasting

  • One-step forecast
$$ E_t[Y_{t+1}] $$
  • Multi-step forecast
$$ E_t[Y_{t+h}] $$

  • Forecasts are recursively computed

    • First compute $E_t[Y_{t+1}] $ which depends on the $\mathcal{F}_t$ information $Y_t, Y_{t-1}, \ldots, \hat{\epsilon}_{t}, \hat{\epsilon}_{t-1} \ldots$
    • Then compute $E_t[Y_{t+2}] $ which depends on $E_t[Y_{t+1}]$ and $\mathcal{F}_t$ information
    • Repeat until $E_t[Y_{t+h}] $

  • Forecasts from covariance stationary time series are mean reverting

AR(1) Model Fitting

$$ Y_t = \phi_0 + \phi_1 Y_{t-1} + \epsilon_t $$
In [5]:
res = SARIMAX(m2_growth, order=(1, 0, 0), trend="c").fit()
summary(res)
coef std err z P>|z| [0.025 0.975]
intercept 0.0021 0.000 11.315 0.000 0.002 0.002
ar.L1 0.6326 0.010 62.755 0.000 0.613 0.652
sigma2 1.221e-05 1.85e-07 65.856 0.000 1.18e-05 1.26e-05

Forecasts

$$ \mathrm{E}_t[Y_{t+h}] = \phi_0 + \phi_1 \mathrm{E}_t[Y_{t+h-1}] + \mathrm{E}_t[\epsilon_{t+h}] $$
In [6]:
fcast = res.forecast(12)
plot(fcast, y=-2)

Manually Constructing Forecasts

  • Forecasts are directly computes using the recursive form
$$ \mathrm{E}_t[Y_{t+h}] = \phi_0 + \phi_1 \mathrm{E}_t[Y_{t+h-1}] + \mathrm{E}_t[\epsilon_{t+h}] $$
  • Rules for computing $E_t[Y_{t+h}]$
    1. $\mathrm{E}_t[\epsilon_{t+h}]=0$ if $h>0$
    2. $\mathrm{E}_t[\epsilon_{t+h}]=\hat{\epsilon}_{t+h}$ if $h\leq 0$
    3. $\mathrm{E}_t[Y_{t+h}]=Y_{t+h}$ if $h\leq 0$
    4. Replace $\mathrm{E}_t[Y_{t+h-j}]$ with its value previously computed
In [7]:
p0 = res.params["intercept"]
p1 = res.params["ar.L1"]
direct = pd.Series(np.zeros(12), index=fcast.index)
for i in range(12):
    if i == 0:
        direct[i] = p0 + p1 * m2_growth.iloc[-1]
    else:
        direct[i] = p0 + p1 * direct[i - 1]

Manual Forecasts

In [8]:
plot(direct)

Recursive Forecast Construction

  • Simple to construct forecasts recursively
In [9]:
t = m2_growth.shape[0]
half = t // 2

forecasts = []
res = SARIMAX(m2_growth.iloc[:half], order=(1, 0, 0), trend="c").fit()
forecasts.append(res.forecast(1))
for i in range(half, t):
    res = res.extend(m2_growth.iloc[i : i + 1])
    forecasts.append(res.forecast(1))
h1 = pd.concat(forecasts)
h1.name = "h1"

Fast one-step forecast construction

  • extend using second half and use get_prediction()
In [10]:
res2 = SARIMAX(m2_growth.iloc[:half], order=(1, 0, 0), trend="c").fit()
res2 = res2.extend(m2_growth.iloc[half:])
fast_h1 = res2.get_prediction().predicted_mean
fast_h1.name = "fast_h1"
pd.concat([h1, fast_h1], axis=1).head()
Out[10]:
h1 fast_h1
1990-06-01 0.001715 0.001715
1990-07-01 0.004789 0.004789
1990-08-01 0.004279 0.004279
1990-09-01 0.005718 0.005718
1990-10-01 0.004645 0.004645

Forecasts and Realizations

In [14]:
fig, ax = plt.subplots(1, 1)
h1.plot(ax=ax)
_ = realizations.plot(ax=ax)

Forecast Errors

  • 1-step should be a $WN$ process
  • $h$-step should be at most a $MA(h-1)$
In [16]:
fe = (realizations - h1).dropna()
plot(fe, 13)

Forecast Error Autocorrelation

  • 1-step should be a $WN$ process
  • $h$-step should be at most a $MA(h-1)$
In [90]:
acf_pacf_plot(fe, 24, size=-2)

Multi-step Forecasts

  • Multi-step forecasts are recursively generated
In [20]:
multistep_forecast_plot()

Multistep forecast errors

In [23]:
plot(fe_12)

Multi-step forecast error dependence

  • May have $h-1$ dependence if well specified
$$ Y_{t+2} - \mathrm{E}_t[Y_{t+2}] = {\color{red}{\epsilon_{t+2}}} + \phi_1 \epsilon_{t+1} $$$$ Y_{t+3} - \mathrm{E}_t[Y_{t+3}] = \epsilon_{t+3} + \phi_1 {\color{red}{\epsilon_{t+2}}} $$
In [24]:
acf_pacf_plot(fe_12, 24, size=13)

Mincer-Zarnowitz Regressions

  • Regress realization on forecast
$$ Y_{t+1} = \alpha + \beta \hat{Y}_{t+1|t} + \eta_{t+1} $$
  • $\alpha$ should be 0
  • $\beta$ should be 1
In [26]:
mz = sm.OLS(realizations, sm.add_constant(combined["h1"])).fit()
summary(mz)
coef std err t P>|t| [0.025 0.975]
const 0.0004 0.000 0.936 0.350 -0.000 0.001
h1 0.8481 0.061 13.985 0.000 0.729 0.967

Testing MZ Coefficients

  • Three possible nulls
    1. $ H_0:\alpha=0$ ($t$-test, use $t$-stat)
    2. $ H_0:\beta=1$ ($t$-test)
    3. $ H_0:\alpha=0\cap \beta=1$ (Wald test)
In [27]:
mz.tvalues["const"]
Out[27]:
0.9362189787611457
In [28]:
(mz.params["h1"] - 1) / mz.bse["h1"]
Out[28]:
-2.504731229244801
In [29]:
stat = mz.wald_test((np.eye(2), [0, 1]), use_f=False)
stat
Out[29]:
<class 'statsmodels.stats.contrast.ContrastResults'>
<Wald test (chi2): statistic=[[9.93955905]], p-value=0.006944678984617448, df_denom=2>

MZ interpretation

In [89]:
mz_plot()

Simpler MZ Testing

  • Regress forecast error
$$ Y_{t+h} - \hat{Y}_{t+h|t} = \alpha + \beta \hat{Y}_{t+h|t} + \eta_{t+h} $$
  • $\alpha=\beta=0$ when correct
In [33]:
mz2 = sm.OLS(combined.fe, sm.add_constant(combined.h1)).fit()
summary(mz2)
coef std err t P>|t| [0.025 0.975]
const 0.0004 0.000 0.936 0.350 -0.000 0.001
h1 -0.1519 0.061 -2.505 0.013 -0.271 -0.033

Updating model parameters

  • Can re-estimate model parameters after each new observation (recursive scheme)
  • Alternatively could keep sample size fixed and update (rolling scheme)
In [35]:
plot(combined[["h1", "recursive", "rolling", "realizations"]].iloc[:-36], y=13)

Diebold-Mariano Testing

  • Test equality of prediction accuracy
  • Test statistic based on the loss difference between two forecasts
$$ \delta_t = l^A_t - l^B_t $$
  • Requires Newey-West covariance since $\{\delta_t \}$ may be autocorrelated
  • Apply to MSE Loss
$$ l_t = (Y_{t+h} - \hat{Y}_{t+h|t})^2 $$
In [36]:
rw = m2_growth.shift(-1)
rw.name = "rw"
combined = pd.concat([combined, rw], axis=1).dropna()
In [37]:
l_a = (combined.realizations - combined.h1) ** 2
l_b = (combined.realizations - combined.rw) ** 2
print(l_a.mean(), l_b.mean())
delta = l_a - l_b

1.947078781444988e-05 2.3835737833687686e-05

DM as an OLS Regression

$$ \delta_t = \beta + \epsilon_t $$
  • $\hat{\beta}=\bar{\delta} $
  • Simple to use Newey-West covariance
In [38]:
mod = sm.OLS(delta, np.ones_like(delta))
dm_res = mod.fit(cov_type="HAC", cov_kwds={"maxlags": int(t ** (1 / 3))})
summary(dm_res)
coef std err z P>|z| [0.025 0.975]
const -4.365e-06 2.16e-06 -2.017 0.044 -8.61e-06 -1.24e-07

Loss difference $\delta_t$

In [39]:
plot(delta)

Mean Absolute Error

  • DM can be used with any loss function
  • MAE is defined
$$ l_t = |Y_{t+h} - \hat{Y}_{t+h|t}| $$
In [40]:
la = np.abs(combined.realizations - combined.h1)
lb = np.abs(combined.realizations - combined.rw)
delta = la - lb
mod = sm.OLS(delta, np.ones_like(delta))
dm_mae_res = mod.fit(cov_type="HAC", cov_kwds={"maxlags": int(t ** (1 / 3))})
summary(dm_mae_res)
coef std err z P>|z| [0.025 0.975]
const -0.0003 0.000 -2.358 0.018 -0.000 -4.27e-05

$\delta_t$ dependence

  • No assumption that models are correctly specified
  • $\delta_t$ may be autocorrelated
In [41]:
acf_pacf_plot(delta, 24, size=13)

Cross-Validation of Time Series

  1. Split data into $k$ contiguous blocks
  2. For $i=1,\ldots,k-1$ using observations in block ${1,2,\ldots,i}$ to forecast values in block $i+1$
  3. Compute out-of-sample $SSE$ for $k-1$ blocks
  4. Select model that produces smallest out-of-sample $SSE$

VIX

In [42]:
plot(vix)

Out-of-sample SSE

In [44]:
print("Splits:")
print(splits)
print("\nOOS SSE:")
oos_sse

Splits:
[146 292 438 584 730]

OOS SSE:
Out[44]:
1     16.848481
2     16.647000
3     16.540600
4     16.426092
5     16.019054
6     16.090477
7     16.052844
8     16.050795
9     16.040110
10    16.056220
11    16.116910
12    16.123724
dtype: float64

Out-of-sample SSE

In [45]:
plot(oos_sse)

CV and IC Selection

In [47]:
plot_sse_ic()

Seasonality

  • A seasonal time series has a deterministic pattern that repeats on an annual basis
In [84]:
plot(housing, y=-2)

Seasonal Autocorrelation Pattern

  • Seasonal data has dynamics at the annual frequency
In [83]:
acf_pacf_plot(housing, 24, size=-2)

Seasonal Differencing

  • Seasonal differencing differences using the seasonal period $s$
$$ \Delta_{s} Y_t = Y_t - Y_{t-s} $$
  • Can reduce or eliminate seasonalities
In [50]:
plot(housing_yoy, y=12)

Seasonal Difference Autocorrelation Pattern

In [51]:
acf_pacf_plot(housing_yoy, 24)

Modeling Seasonal Differences

In [52]:
res = SARIMAX(housing_yoy, order=(1, 0, 0)).fit()
resids = res.resid.iloc[1:]
acf_pacf_plot(resids, 24, size=-2)

Seasonal ARMA Models

  • SARMA models data at both the observational and seasonal frequency
  • In lag polynomial representation
$$ (1-\phi_1 L -\phi_2 L^2) Y_t = \phi_0 + (1+\theta_s L^s)\epsilon_t $$
  • In standard ARMA representation
$$ Y_t = \phi_0 + \phi_1 Y_{t-1} +\phi_2 Y_{t-2} \theta_s \epsilon_{t-s} + \epsilon_t $$$$ SARMA$(P,0,Q)\times(P_s,0,Q_s,s) $$
In [53]:
res = SARIMAX(housing_yoy, order=(2, 0, 0), seasonal_order=(0, 0, 1, 12)).fit()
summary(res)
coef std err z P>|z| [0.025 0.975]
ar.L1 0.6809 0.034 20.284 0.000 0.615 0.747
ar.L2 0.2824 0.034 8.233 0.000 0.215 0.350
ma.S.L12 -0.8795 0.017 -50.520 0.000 -0.914 -0.845
sigma2 0.0083 0.000 21.791 0.000 0.008 0.009

SARMA Residual Diagnostics

In [82]:
acf_pacf_plot(res.resid.iloc[13:], 24)

Extended Dynamics $(1,0,1)\times(1,0,1)_{12}$

  • In lag polynomial representation
$$ (1-\phi_1 L)(1-\phi_s L^s)Y_t = \phi_0 + (1+\theta_1 L)(1+\theta_s L^s)\epsilon_t $$
  • In standard ARMA representation
$$ Y_t = \phi_0 + \phi_1 Y_{t-1} +\phi_s Y_{t-s} - \phi_1\phi_s Y_{t-s-1} + \theta_1 \epsilon_{t-1} +\theta_s \epsilon_{t-s} + \theta_1\theta_s \epsilon_{t-s-1} + \epsilon_t $$
In [55]:
res = SARIMAX(housing_raw, order=(1, 0, 1), seasonal_order=(1, 0, 1, 12)).fit()
summary(res)
coef std err z P>|z| [0.025 0.975]
ar.L1 0.9994 0.001 1449.669 0.000 0.998 1.001
ma.L1 -0.3196 0.031 -10.258 0.000 -0.381 -0.259
ar.S.L12 0.9987 0.001 1455.660 0.000 0.997 1.000
ma.S.L12 -0.8956 0.016 -54.434 0.000 -0.928 -0.863
sigma2 0.0083 0.000 22.227 0.000 0.008 0.009

Extended Model Residual Diagnostics

In [81]:
acf_pacf_plot(res.resid.iloc[13:], 24)

Incorporating differencing

  • SARIMA - Seasonal Autoregressive Integrated Moving Average
$$ \text{SARIMA} (P,D,Q)\times(P_s,D_s,Q_s,s)$$$$ \Phi(L)\Phi_s(L)(1-L)^D(1-L^s)^{D_s} Y_t = \phi_0 + \Theta(L)\Theta_s(L)\epsilon_t $$
  • AR $$\Phi(L) = $1-\phi_1 L - \phi_2 L^2 - \ldots -\phi_P L^P$$
  • Seasonal AR: $$\Phi_s(L) = 1-\phi_s L^s - \phi_{2s} L^{2s} - \ldots -\phi_{P_s s} L^{P_s s}$$
  • MA: $$\Theta(L) = 1+\theta_1 L + \theta_2 L^2 + \ldots +\phi_Q L^Q$$
  • Seasonal MA: $$\Theta_s(L) = 1+\theta_s L^s + \theta_{2s} L^{2s} + \ldots +\theta_{Q_s s} L^{Q_s s}$$

Estimating SARIMA Models

In [57]:
res = SARIMAX(housing_raw, order=(1, 0, 1), seasonal_order=(0, 1, 1, 12)).fit()
summary(res)
coef std err z P>|z| [0.025 0.975]
ar.L1 0.9778 0.008 126.962 0.000 0.963 0.993
ma.L1 -0.3128 0.033 -9.354 0.000 -0.378 -0.247
ma.S.L12 -0.8774 0.018 -48.040 0.000 -0.913 -0.842
sigma2 0.0083 0.000 21.771 0.000 0.008 0.009

SARIMA$(1,0,1)\times(0,1,1)_{12}$ Residual Analysis

In [58]:
acf_pacf_plot(res.resid.iloc[13:], 24)

Enforcing Unit Roots

  • Set both differences to 1

    • $D=1$
    • $D_s=1$

  • Model is

$$ (1-L)(1-L^s)Y_t = \theta_s \epsilon_{t-s} + \epsilon_t $$$$ \Delta\Delta_s Y_t = \theta_s \epsilon_{t-s} + \epsilon_t $$
In [59]:
res = SARIMAX(housing_raw, order=(0, 1, 1), seasonal_order=(0, 1, 1, 12)).fit()
summary(res)
coef std err z P>|z| [0.025 0.975]
ma.L1 -0.3246 0.031 -10.361 0.000 -0.386 -0.263
ma.S.L12 -0.8774 0.019 -47.049 0.000 -0.914 -0.841
sigma2 0.0084 0.000 22.025 0.000 0.008 0.009

Double Difference Residual Diagnostics

In [60]:
acf_pacf_plot(res.resid.iloc[13:], 24)

Seasonal Dummies

  • Alternative approach to SARIMA
  • Model shifts as a deterministic processes
  • One dummy for each of $s-1$ periods
    • Avoid dummy variable trap
In [61]:
month = housing_raw.index.strftime("%b")
dummies = pd.get_dummies(month)
dummies = dummies[
    ["Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
]
dummies.index = housing_raw.index
dummies.head()
Out[61]:
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1960-01-01 0 0 0 0 0 0 0 0 0 0 0
1960-02-01 1 0 0 0 0 0 0 0 0 0 0
1960-03-01 0 1 0 0 0 0 0 0 0 0 0
1960-04-01 0 0 1 0 0 0 0 0 0 0 0
1960-05-01 0 0 0 1 0 0 0 0 0 0 0

Incorporating Dummies

In [62]:
res = SARIMAX(housing_raw, order=(2, 1, 0), exog=dummies, trend="c").fit()
summary(res)
coef std err z P>|z| [0.025 0.975]
intercept 0.0002 0.004 0.046 0.963 -0.007 0.008
Feb 0.0358 0.012 2.965 0.003 0.012 0.059
Mar 0.3075 0.012 24.776 0.000 0.283 0.332
Apr 0.4289 0.015 29.517 0.000 0.400 0.457
May 0.4669 0.018 26.261 0.000 0.432 0.502
Jun 0.4697 0.019 25.309 0.000 0.433 0.506
Jul 0.4328 0.019 23.266 0.000 0.396 0.469
Aug 0.4117 0.019 22.228 0.000 0.375 0.448
Sep 0.3657 0.017 21.803 0.000 0.333 0.399
Oct 0.3921 0.015 26.254 0.000 0.363 0.421
Nov 0.2170 0.013 16.944 0.000 0.192 0.242
Dec 0.0502 0.010 5.242 0.000 0.031 0.069
ar.L1 -0.2675 0.033 -8.115 0.000 -0.332 -0.203
ar.L2 -0.1107 0.034 -3.277 0.001 -0.177 -0.045
sigma2 0.0087 0.000 21.345 0.000 0.008 0.010

Seasonal Dummy Model Residuals

In [63]:
resid = res.resid.iloc[2:]
plot(resid, y=-2)

Seasonal Dummy Model Residuals Diagnostics

In [64]:
acf_pacf_plot(resid, 24)

Unit Root Testing

  • Test $Y_t$ for a unit root
  • If null is not rejected, ensure that all required deterministic terms are included
  • If null is still not rejected, difference and test $\Delta Y_t$
  • Positive $Y_t$ series should usually be transformed with $\ln$
  • Select lag length of $\Delta Y_{t-j}$ terms using IC, usually AIC
  • $H_0:\gamma=0$, $H_1:\gamma<0$
$$ Y_t = \delta_0 + \delta_1 t + \gamma Y_{t-1} + \sum_{i=1}^P \Delta Y_{t-i} + \epsilon_t $$
  • Deterministic terms are needed to remove deterministic components from $Y_{t-1}$

The Dickey-Fuller distributions

In [66]:
adf_cv_plot()

Testing Default Premium

In [67]:
from arch.unitroot import ADF

adf = ADF(default)
adf.summary()
Out[67]:
Augmented Dickey-Fuller Results
Test Statistic -3.866
P-value 0.002
Lags 10


Trend: Constant
Critical Values: -3.44 (1%), -2.87 (5%), -2.57 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

Testing Curvature

In [68]:
adf = ADF(curve)
adf.summary()
Out[68]:
Augmented Dickey-Fuller Results
Test Statistic -4.412
P-value 0.000
Lags 19


Trend: Constant
Critical Values: -3.44 (1%), -2.87 (5%), -2.57 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

Testing Industrial Production

In [69]:
adf = ADF(np.log(orig.INDPRO), trend="c")
adf.summary()
Out[69]:
Augmented Dickey-Fuller Results
Test Statistic -2.186
P-value 0.211
Lags 4


Trend: Constant
Critical Values: -3.44 (1%), -2.87 (5%), -2.57 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

ADF Regression Results

In [70]:
summary(adf.regression)
coef std err t P>|t| [0.025 0.975]
Level.L1 -0.0017 0.001 -2.186 0.029 -0.003 -0.000
Diff.L1 0.3637 0.037 9.860 0.000 0.291 0.436
Diff.L2 -0.1110 0.039 -2.832 0.005 -0.188 -0.034
Diff.L3 0.0447 0.039 1.138 0.255 -0.032 0.122
Diff.L4 0.0217 0.037 0.589 0.556 -0.051 0.094
const 0.0083 0.003 2.579 0.010 0.002 0.015

Increasing the deterministic order

In [71]:
adf = ADF(np.log(orig.INDPRO), trend="ct")
adf
Out[71]:
Augmented Dickey-Fuller Results
Test Statistic -1.831
P-value 0.690
Lags 6


Trend: Constant and Linear Time Trend
Critical Values: -3.97 (1%), -3.42 (5%), -3.13 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

Testing the difference

  • The Industrial Productivity Index is $I(1)$ since 1 difference makes it stationary
In [72]:
delta_indpro = np.log(orig.INDPRO).diff().dropna()
adf = ADF(delta_indpro, trend="c")
adf
Out[72]:
Augmented Dickey-Fuller Results
Test Statistic -11.945
P-value 0.000
Lags 3


Trend: Constant
Critical Values: -3.44 (1%), -2.87 (5%), -2.57 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

The importance of time trends

  • Incorrect trend specification results in no power to reject false nulls
  • Always find a unit root in trending time series
In [74]:
plot(trending, 13)

Under-specifying Deterministic Terms

  • Do not use model with no deterministic terms on real data unless $Y_t$ is known to be mean 0
In [75]:
ADF(trending, trend="n")
Out[75]:
Augmented Dickey-Fuller Results
Test Statistic 1.934
P-value 0.988
Lags 9


Trend: No Trend
Critical Values: -2.58 (1%), -1.94 (5%), -1.62 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

Under-specifying Deterministic Terms

In [76]:
ADF(trending, trend="c")
Out[76]:
Augmented Dickey-Fuller Results
Test Statistic -1.146
P-value 0.696
Lags 9


Trend: Constant
Critical Values: -3.46 (1%), -2.87 (5%), -2.57 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

Correctly specifying Deterministic Terms

In [77]:
ADF(trending, trend="ct")
Out[77]:
Augmented Dickey-Fuller Results
Test Statistic -6.790
P-value 0.000
Lags 0


Trend: Constant and Linear Time Trend
Critical Values: -4.00 (1%), -3.43 (5%), -3.14 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

Over-specifying Deterministic Terms

In [78]:
ADF(y, trend="ctt")
Out[78]:
Augmented Dickey-Fuller Results
Test Statistic -6.885
P-value 0.000
Lags 0


Trend: Constant, Linear and Quadratic Time Trends
Critical Values: -4.42 (1%), -3.86 (5%), -3.57 (10%)
Null Hypothesis: The process contains a unit root.
Alternative Hypothesis: The process is weakly stationary.

Incorporating Time Trends into ARMA Models

  • Can jointly estimate trends and stationary components of ARMA models
$$ Y_t = \phi_0 + \delta_1 t + \phi_1 Y_{t-1} + \epsilon_t $$
In [79]:
res = SARIMAX(trending, order=(1, 0, 0), trend="ct").fit()
summary(res)
coef std err z P>|z| [0.025 0.975]
intercept 0.0796 0.116 0.687 0.492 -0.148 0.307
drift 0.0255 0.004 6.149 0.000 0.017 0.034
ar.L1 0.6822 0.050 13.534 0.000 0.583 0.781
sigma2 0.9213 0.080 11.451 0.000 0.764 1.079

Time Trend Model Residuals

In [80]:
plot(res.resid.iloc[1:])

Next Week

  • Univariate Volatility Modeling